CN102884564B - Risk degree calculation device - Google Patents
Risk degree calculation device Download PDFInfo
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- CN102884564B CN102884564B CN201080066680.7A CN201080066680A CN102884564B CN 102884564 B CN102884564 B CN 102884564B CN 201080066680 A CN201080066680 A CN 201080066680A CN 102884564 B CN102884564 B CN 102884564B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q5/00—Arrangement or adaptation of acoustic signal devices
- B60Q5/005—Arrangement or adaptation of acoustic signal devices automatically actuated
- B60Q5/006—Arrangement or adaptation of acoustic signal devices automatically actuated indicating risk of collision between vehicles or with pedestrians
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
- B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/865—Combination of radar systems with lidar systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/867—Combination of radar systems with cameras
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Traffic Control Systems (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The present invention relates to a kind of risk degree calculation device.Motive objects risk calculus portion (22) according to by be arranged on this vehicle (100) surrounding multiple grids (M) intersection point in the risk factor that produces of the object of each point of intersection, to the risk factor that the motive objects by each point of intersection produces, calculate in the aggregate value of all point of intersection of grid (M).Thereby, it is possible to obtain by other vehicles in travelling and in stopping or walking and the degree of danger that produces of the motive objects of the pedestrian that halts etc.In addition, motive objects risk calculus portion (22) passes through from the aggregate value of the risk factor produced by object, deduct the risk factor that irremovable object produces by being fixed on each point of intersection, in the aggregate value of all point of intersection of grid (M), thus the aggregate value of the risk factor produced by motive objects in all point of intersection of grid (M) to be calculated.Thus, due to motive objects risk calculus portion (22) without the need to each point of intersection at grid (M) to can the object of movement and irremovable object distinguish, therefore, it is possible to calculated by the aggregate value of less computational load to the risk factor produced by motive objects.
Description
Technical field
The present invention relates to a kind of risk degree calculation device, particularly relate to the risk degree calculation device that the risk factor for the surrounding to this vehicle calculates.
Background technology
Propose a kind of security in order to improve traveling, and to the device that the potential risk factor of the surrounding of vehicle calculates.Such as, in patent documentation 1, disclose a kind of following vehicle steering operation assisting device, that is, the potential risk kinetic energy by this vehicle caused, by travelling rule and the potential risk caused, the potential risk caused by the degree of closeness with barrier and by jumping the queue vehicle and the potential risk that causes calculates.In the device of patent documentation 1, in the potential risk caused by kinetic energy and the potential risk caused by degree of closeness, the side that selective value is larger, and apply continually varying counter-force to accelerator pedal and bearing circle.In the device of patent documentation 1, by travelling rule and the potential risk that causes and by jumping the queue vehicle and in the potential risk that causes, prioritizing selection by jumping the queue vehicle and the potential risk that causes, and applies to accelerator pedal and bearing circle with the counter-force of stepped increase further.
At first technical literature
Patent documentation
Patent documentation 1: Japanese Unexamined Patent Publication 2008-6922 publication
Summary of the invention
Invent problem to be solved
But, in technology as described above, about barrier, according to the information obtained by the sensor as laser radar, carrying out disturbance in judgement thing is motive objects as other vehicles, or the resting as buildings, and carries out computing to the risk factor of this barrier.Therefore, exist and be used for carrying out the larger shortcoming of the computational load of computing to the risk factor of motive objects.In addition, about resting, also not carrying out this resting is be positioned at there, the object be fixed as buildings all the time, or as other vehicles can the judgement of object of movement.
In technology as described above, in order to the information that basis is obtained by sensor, carrying out disturbance in judgement thing is motive objects or resting, needs the detailed process carried out the information obtained by sensor.And, when other vehicles also implemented to be in static object and just stop, be still fixed and the judgement of irremovable buildings time, need higher computational load.
The present invention completes under the circumstances, its object is to, provide a kind of can by less computational load, to by can the risk degree calculation device that calculates of the risk factor that produces of the object of movement.
For solving the method for problem
The present invention is following risk degree calculation device, it possesses motive objects risk factor computing unit, described motive objects risk factor computing unit according to by be arranged on this vehicle surrounding multiple places in the risk factor that produces of the object in each place, to by each place can the object of the movement motive objects risk factor that produces, calculate in the aggregate value at all place places, motive objects risk factor computing unit passes through the risk factor produced from the object by each place, in the aggregate value at all place places, deduct irremovable object produces by being fixed on each place fixture risk factor, in the aggregate value at all place places, thus the aggregate value of motive objects risk factor at all place places is calculated.
According to this structure, motive objects risk factor computing unit according to by be arranged on this vehicle surrounding multiple places in the risk factor that produces of the object in each place, to by each place can the motive objects risk factor that produces of the object of movement, calculate in the aggregate value at all place places.Thereby, it is possible to obtain by other vehicles in travelling and in stopping or walking and the degree of danger that produces of the motive objects of the pedestrian that halts etc.In addition, motive objects risk factor computing unit by produce from the object by each place, in the aggregate value of the risk factor in all places, deduct the fixture risk factor that irremovable object produces by being fixed on each place, aggregate value at all place places, thus the aggregate value of motive objects risk factor at all place places to be calculated.Thus, due to motive objects risk factor computing unit without the need at each place place to can the object of movement and irremovable object distinguish, therefore, it is possible to calculated by the aggregate value of less computational load to motive objects risk factor.
Now, can be in the following way, namely, motive objects risk factor computing unit is preset according to each position be directed on map, the aggregate value of fixture risk factor at all place places, and the position of this vehicle on map, obtain the aggregate value of fixture risk factor at all place places.
According to this structure, motive objects risk factor computing unit is preset according to each position be directed on map, the aggregate value of fixture risk factor at all place places, and the position of this vehicle on map, obtain the aggregate value of fixture risk factor at all place places.Thus, because motive objects risk factor computing unit is without the need to when this vehicle drives to this position at every turn, all irremovable object is detected and the risk factor produced by this object is calculated, therefore, it is possible to calculated by the aggregate value of less computational load to fixture risk factor.
Now, can be in the following way, that is, motive objects risk factor computing unit is according to the object of the carrying out movement detected at each place place, is preset, fixture risk factor revises in the aggregate value at all place places to each position be directed on map.
When being newly provided with buildings or works at this place place, between the fixture risk factor be preset and the fixture risk factor of reality, difference will be produced.But, according to this structure, motive objects risk factor computing unit, according to the object of the carrying out movement detected at each place place, is preset each position be directed on map, fixture risk factor revises in the aggregate value at all place places.Thus, even if also can tackle being newly provided with in the situations such as buildings.
In addition, can be in the following way, namely, also possesses Motion prediction unit, described Motion prediction unit to the surrounding of this vehicle, can the action of object of movement predict, Motion prediction unit every predetermined discrete time to the surrounding of this vehicle, can the action of object of movement predict, and motive objects risk factor computing unit calculate, motive objects risk factor is larger in the aggregate value at all place places, then more shorten discrete time.
According to this structure, Motion prediction unit to the surrounding of this vehicle, can the action of object of movement predict.In addition, Motion prediction unit every predetermined discrete time to the surrounding of this vehicle, can the action of object of movement predict, and that motive objects risk factor computing unit calculates, motive objects risk factor is larger in the aggregate value at all place places, then more shorten discrete time.Thus, when the aggregate value of motive objects risk factor is larger, security can be improved by improving the precision of prediction, and, when the aggregate value of motive objects risk factor is less, computational load can be reduced while guarantee security.
In addition, can be in the following way, namely, also possesses Motion prediction unit, described Motion prediction unit to the surrounding of this vehicle, can the action of object of movement predict, Motion prediction unit can calculate by the object of the movement probability that is present in each preposition place the surrounding of this vehicle, and motive objects risk factor computing unit calculate, motive objects risk factor is larger in the aggregate value at all place places, then more increase the dispersion of the distribution of probability.
According to this structure, Motion prediction unit to the surrounding of this vehicle, can the action of object of movement predict.In addition, Motion prediction unit can calculate by the object of the movement probability that is present in each preposition place the surrounding of this vehicle, and that motive objects risk factor computing unit calculates, motive objects risk factor is larger in the aggregate value at all place places, then the dispersion of the distribution of probability more increases.Thus, more when the aggregate value of motive objects risk factor is larger, more safer prediction can be implemented.
In addition, can be in the following way, namely, also possesses driving auxiliary unit, the driving of described driving auxiliary unit to the driver of this vehicle is assisted, and that motive objects risk factor computing unit calculates, motive objects risk factor is higher in the aggregate value at all place places, then drive auxiliary unit more improve the degree that the driving of the driver of this vehicle is assisted.
According to this structure, drive the driving of auxiliary unit to the driver of this vehicle and assist.In addition, that motive objects risk factor computing unit calculates, motive objects risk factor is higher in the aggregate value at all place places, then drive auxiliary unit and more improve the degree that the driving of the driver of this vehicle is assisted.Thereby, it is possible to realize the auxiliary of the driving corresponding with the aggregate value of motive objects risk factor.
The effect of invention
According to risk degree calculation device of the present invention, can by less computational load to by can the risk factor that produces of the object of movement calculate.
Accompanying drawing explanation
The block diagram of the structure of the drive assistance device of Fig. 1 involved by expression first embodiment.
The process flow diagram of the action of the drive assistance device of Fig. 2 involved by expression first embodiment.
The figure of the grid of Fig. 3 involved by expression first embodiment.
Fig. 4 represents the figure showing the reflection spot of laser radar in the grid of Fig. 3.
The block diagram of the structure of the drive assistance device of Fig. 5 involved by expression second embodiment.
The process flow diagram of the action of the drive assistance device of Fig. 6 involved by expression second embodiment.
The block diagram of the structure of the drive assistance device of Fig. 7 involved by expression the 3rd embodiment.
Fig. 8 for expression in the Motion prediction operational part of Fig. 7 institute's computing, the vertical view that there is probability distribution of other vehicles.
Embodiment
Below, with reference to accompanying drawing, the first embodiment of the present invention is described.Present embodiment is, risk degree calculation device of the present invention is applied in the mode in drive assistance device.As shown in Figure 1, the drive assistance device 10a of present embodiment possesses: laser radar 12, grid operational part 14, overall risk operational part 16, GPS18, works risk memory storage 20, motive objects risk calculus portion 22 and information provider unit 24.
Laser radar (Lidar) 12 measures relative to the scattered light produced with the irradiation of the laser of pulse type luminescence for passing through, thus detect the object at each place place of the surrounding being arranged on this vehicle, and no matter this object is motive objects or fixture.
Grid operational part 14 is for setting clathrate region, i.e. grid around this vehicle, and the risk factor (Risk) of each point of intersection to this grid calculates.Overall risk operational part 16 adds up to for the risk factor of each point of intersection to grid, thus calculates the aggregate value of the risk factor that the object of the surrounding by this vehicle produces.
GPS(Global Positioning System: GPS) 18 for implementing the position finding to this vehicle.Works risk memory storage 20 is following database, that is, be directed to each place on map and store the database of the aggregate value of risk factor when this vehicle is positioned at this place, that produced by the fixture such as irremovable buildings of each point of intersection of grid.
As described later, motive objects risk calculus portion 22 for calculate according to overall risk operational part 16, the aggregate value of risk factor that produced by the object of the surrounding of this vehicle, with extract from works risk memory storage 20, the aggregate value of risk factor that produced by fixture, the aggregate value of the risk factor that the motive objects of the surrounding by this vehicle produces is calculated.
Information provider unit 24 for motive objects risk calculus portion 22 is calculated, the aggregate value of risk factor that produced by motive objects, be shown to the driver of this vehicle.Specifically, information provider unit 24 is display or loudspeaker.Information provider unit 24 is except being prompted to the driver of this vehicle by the aggregate value of the risk factor produced by motive objects by image or sound, also according to the aggregate value of the risk factor produced by this motive objects, the driving of the driver of this vehicle is assisted.
Below, the action of the drive assistance device 10a of present embodiment is described.As shown in Figure 2, drive assistance device 10a implement to be undertaken by laser radar 12, detection (S11) to the object of the surrounding of this vehicle.The reflection spot of the laser irradiated by laser radar 12 is shown (S12) on grid by the grid operational part 14 of drive assistance device 10a.
As shown in Figure 3, grid operational part 14 setting clathrate region, i.e. grid M around this vehicle 100.The size in each clathrate region of grid M is set to, the size of the unit vector i in X-direction and the unit vector j in Y direction.As shown in Figure 4, be there is the point of intersection that probability P shows grid M by grid operational part 14 in the object involved by reflection spot.
At this, there is probability P and refer to, object is present in the probability of this point of intersection of grid M.Grid operational part 14 under the condition of the fiduciary level of detection and the dispersion degree of probability distribution of considering laser radar 12, and carries out computing to the probability P (x, y) that exists of each point of intersection of grid M.
Overall risk operational part 16 passes through the reflection spot of the laser irradiated by laser radar 12, and carries out computing (S13) to the risk factor of each point of intersection of grid M in the aggregate value of all point of intersection.Overall risk operational part 16 is come the risk factor of each point of intersection of the grid M aggregate value R in all point of intersection by following formula (1)
allcalculate.In following formula (1), f(P(x, y)) represent conversion formula from there is probability P and converting to risk factor, such as f(P)=A × P like this, make to there is probability P and be multiplied by fixing gain A.
R
all＝Σ｛f（P（ｘ，y））｝（1）
Following aggregate value is previously stored with in works risk memory storage 20, namely, assuming that when this vehicle 100 is positioned at each position on map, the risk factor produced by the irremovable fixture of each point of intersection being positioned at grid M is in the aggregate value of all point of intersection of grid M.Motive objects risk calculus portion 22 extracts the aggregate value (S14) of risk factor that correspond to the pass the position of this vehicle detected by GPS20, that produced by fixture from works risk memory storage 20.
Motive objects risk calculus portion 22 is from the aggregate value R of the risk factor of each point of intersection of the grid M calculated among S13
allin, deduct extract in S14, corresponding to the aggregate value of the risk factor produced by fixture of the position of this vehicle, thus the aggregate value (S15) of the risk factor produced by motive objects in all point of intersection of grid M to be calculated.
The aggregate value of the risk factor produced by motive objects is shown to the driver (S16) of this vehicle 100 by information provider unit 24.The aggregate value of the risk factor produced by motive objects is larger, then information provider unit 24 more can increase the volume to the frequency of driver with information, the size of image display, the brightness of image display and sound.Thereby, it is possible to promote that driver implements safe action.Or, drive assistance device 10a also can as following component, that is, the aggregate value of the risk factor produced by motive objects is larger, then more the acceleration operation of this vehicle 100, brake operating and steering operation are implemented to the component of the enforceable intervention undertaken by actuator.
According to the present embodiment, motive objects risk calculus portion 22 according to by be arranged on this vehicle 100 surrounding multiple grid M intersection point in the risk factor that produces of the object of each point of intersection, to the risk factor that the motive objects by each point of intersection produces, calculate in the aggregate value of all point of intersection of grid M.Thereby, it is possible to obtain by other vehicles in travelling and in stopping or walking and the degree of danger that produces of the motive objects such as the pedestrian that halts.In addition, motive objects risk calculus portion 22 passes through from the aggregate value of the risk factor produced by object, deduct the risk factor that irremovable object produces by being fixed on each point of intersection, in the aggregate value of all point of intersection of grid M, thus the aggregate value of the risk factor produced by motive objects in all point of intersection of grid M to be calculated.Thus, due to motive objects risk calculus portion 22 without the need to each point of intersection at grid M to can the object of movement and irremovable object distinguish, therefore, it is possible to calculate the aggregate value of the risk factor produced by motive objects at short notice with less computational load.
In the present embodiment, by laser radar 12 to be arranged on this vehicle 100 surrounding multiple grid M intersection point in the object of each point of intersection detect.By laser radar 12, to carry out judgment object be motive objects or fixture is more difficult.But, higher to whether there is the precision that object judges by laser radar 12.In the present embodiment, due to without the need to being distinguished motive objects and fixture by laser radar 12, as long as and higher to the precision of the detection whether existed, therefore, it is possible to effectively utilize the characteristic of laser radar 12.
In addition, according to the present embodiment, the dangerous operational part 22 of motive objects is directed to the risk factor that each position on map is preset, produced by fixture according to being stored in works risk memory storage 20, with the position of this vehicle 100 on map, obtain the risk factor that produced by the fixture aggregate value in all point of intersection of grid M.Thus, because motive objects risk calculus portion 22 is when this vehicle 100 drives to this position at every turn, all without the need to detecting irremovable object and calculating the risk factor produced by this object, therefore, it is possible to calculate with the aggregate value of less computational load to the risk factor produced by fixture.
And according to the present embodiment, the driving of information provider unit 24 to the driver of this vehicle 100 is assisted.In addition, risk factor that motive objects risk calculus portion 22 calculates, that produced by motive objects is higher in the aggregate value of all point of intersection of grid M, then information provider unit 24 more improves the degree of assisting the driving of the driver of this vehicle 100.Thereby, it is possible to realize the auxiliary of the driving corresponding with the aggregate value of the risk factor produced by motive objects.
Below, the second embodiment of the present invention is described.In the present embodiment, according to the object of the carrying out movement detected by each point of intersection of grid M, the risk factor implementing to be preset each position be directed on map in works risk memory storage 20, to be produced by fixture is revised, different from above-mentioned first embodiment in this point.
As shown in Figure 5, the drive assistance device 10b of present embodiment, except the textural element of the drive assistance device 10a of above-mentioned first embodiment, also possesses video camera 26, millimetre-wave radar 28, motive objects detection calculations portion 30 and works risk study operational part 32.Video camera 26 and millimetre-wave radar 28 are for detecting the motive objects of surrounding or fixture that are present in this vehicle 100.Video camera 26 can adopt easily to the stereocamera etc. that object detects apart from the change of the distance of this vehicle 100.Millimetre-wave radar 28 by detecting the Doppler displacement of reflection wave, thus can detect the change of object apart from the distance of this vehicle 100.
Motive objects detection calculations portion 30 for according to the testing result that detected by video camera 26 and millimetre-wave radar 28, is determined the motive objects of point of intersection and fixture being present in grid M.Works risk study operational part 32, for determined according to motive objects detection calculations portion 30, that be present in the point of intersection of grid M motive objects and fixture, is revised the value be stored in works risk memory storage 20.
Below, the action of the drive assistance device 10b of present embodiment is described.As shown in Figure 6, video camera 26, millimetre-wave radar 28 and motive objects detection calculations portion 30 are detected (S21) the motive objects of surrounding or fixture that are present in this vehicle 100.
Works risk study operational part 32 is determined according to motive objects detection calculations portion 30, the motive objects or the fixture that are present in the point of intersection of grid M, to the position of this vehicle 100, the risk factor that produced by fixture calculates in the aggregate value of all point of intersection of grid M.Works risk study operational part 32 is to the aggregate value of the risk factor produced by fixture being stored in position in works risk memory storage 20, this vehicle 100, and the aggregate value of risk factor that the fixture newly determined by motive objects detection calculations portion 30 produces compares (S22).
When being stored in the aggregate value of risk factor in works risk memory storage 20, that produced by fixture, and between the aggregate value of risk factor that produces of the fixture newly to be determined by motive objects detection calculations portion 30, when there is the difference of more than predetermined threshold value (S22), works risk study operational part 32, by being stored in the aggregate value of risk factor in works risk memory storage 20, that produced by fixture, is modified to the aggregate value (S23) of the risk factor that the fixture newly determined by motive objects detection calculations portion 30 produces.
Works risk study operational part 32 can when this vehicle 100 be at every turn by this place, implements the determination to motive objects and fixture undertaken by motive objects detection calculations portion 30.For such as previous and this inferior repeatedly, the result of the determination for motive objects and fixture of being undertaken by motive objects detection calculations portion 30, it is more the result of new determination, then works risk study operational part 32 is more evaluated as important, thus can revise the aggregate value being stored in risk factor in works risk memory storage 20, that produced by fixture.Thereby, it is possible to improve the precision of the study undertaken by works risk study operational part 32.
When new buildings or works are arranged on this place, are preset between the risk factor produced by fixture in works risk memory storage 20 and the risk factor produced by the fixture of reality and will produce difference.But, according to the present embodiment, works risk study operational part 32 is according to the motive objects detected by each point of intersection of grid M and fixture, and the aggregate value of the risk factor be preset each position be directed on map, produced by fixture is revised.Thus, even if also can tackle when being newly provided with buildings etc.
Below, the 3rd embodiment of the present invention is described.In the present embodiment, to can the action of object of movement carry out in prediction this point around this vehicle 100, different from above-mentioned first embodiment.As shown in Figure 7, the drive assistance device 10c of present embodiment, except the textural element of the drive assistance device 10a of above-mentioned first embodiment, also possesses Motion prediction operational part 34.Motion prediction operational part 34 is by Monte Carlo method, and the probability that exists every predetermined discrete time, the motive objects of the surrounding being present in this vehicle 100 being present in each point of intersection of grid M calculates.
As shown in Figure 8, be set as that other vehicles 200 are just travelling on the front of this vehicle 100.Motion prediction operational part 34 is every prediction Δ T sample time, and by Monte Carlo method (particle filter), there is probability distribution P(t+ Δ T in what be present in each point of intersection of grid M to other vehicles 200), there is probability distribution P(t+2 Δ T) calculate.In fig. 8, there is the average by mean P av(t+ Δ T of probability), mean P av(t+2 Δ T) represent.In fig. 8, there is the dispersion of probability by disperseing Pv(t+ Δ T), dispersion Pv(t+2 Δ T) represent.
The aggregate value of risk factor that motive objects risk calculus portion 22 calculates, that produced by motive objects is fewer, then Motion prediction operational part 34 more increases prediction Δ T sample time, or, more reduce random number frequency N, thus more reduce dispersion Pv(t+ Δ T), dispersion Pv(t+2 Δ T).In addition, the aggregate value of risk factor that motive objects risk calculus portion 22 calculates, that produced by motive objects is larger, then Motion prediction operational part 34 more reduces prediction Δ T sample time, or, more increase random number frequency N, thus more increase dispersion Pv(t+ Δ T), dispersion Pv(t+2 Δ T).Information displaying according to the action of other vehicles 200 doped by Motion prediction operational part 34, and is given the driver of this vehicle 100 by information provider unit 24.
In the present embodiment, the action of Motion prediction operational part 34 to the motive objects of other vehicle 200 grades of the surrounding of this vehicle 100 is predicted.Motion prediction operational part 34 calculates the probability that the motive objects of the surrounding of this vehicle 100 is present in each preposition place every prediction Δ T sample time, and the aggregate value of risk factor that motive objects risk calculus portion 22 calculates, that produced by motive objects is larger, then more shorten prediction Δ T sample time.Thus, when the aggregate value of the risk factor produced by motive objects is larger, security can be improved by improving the precision of prediction, and, when the aggregate value of motive objects risk factor is less, computational load can be reduced while guarantee security.
In addition, in the present embodiment, the aggregate value of risk factor that motive objects risk calculus portion 22 calculates, that produced by motive objects is larger, then Motion prediction operational part 34 is got over increase and be there is probability distribution P(t+ Δ T) dispersion Pv(t+ Δ T).Thus, when the aggregate value of the risk factor produced by motive objects is larger, then more can implement safer prediction.
Above, although be illustrated embodiments of the present invention, the present invention is not limited to above-mentioned embodiment, but can carry out various change.
Utilizability in industry
According to risk degree calculation device of the present invention, can with less computational load to by can the risk factor that produces of the object of movement calculate.
Symbol description
10a, 10b, 10c drive assistance device
12 laser radars
14 grid operational parts
16 overall risk operational parts
18 GPS
20 works risk memory storages
22 motive objects risk calculus portions
24 information provider units
26 video cameras
28 millimetre-wave radars
30 motive objects detection calculations portions
32 works risk study operational parts
34 Motion prediction operational parts
100 vehicles
200 other vehicles
Claims (9)
1. a risk degree calculation device, wherein,
Possesses motive objects risk factor computing unit, described motive objects risk factor computing unit according to by be arranged on this vehicle surrounding multiple places in the risk factor that produces of the object in each place, to by each described place can the motive objects risk factor that produces of the object of movement, calculate in the aggregate value at all described place places
Described motive objects risk factor computing unit is preset according to each position be directed on map, be fixed on each described place place and the aggregate value of fixture risk factor at all described place places that irremovable object produces, and described vehicle position on the map, obtain the aggregate value of described fixture risk factor at all described place places, and not at each place place to can the object of movement and irremovable object distinguish, by the risk factor that produces from the object by each described place, in the aggregate value at all described place places, deduct the aggregate value of described fixture risk factor at all described place places, thus the aggregate value of described motive objects risk factor at all described place places is calculated.
2. risk degree calculation device as claimed in claim 1, wherein,
Described motive objects risk factor computing unit, according to the object of the carrying out movement detected at each described place place, is preset each position be directed on described map, described fixture risk factor revises in the aggregate value at all described place places.
3. risk degree calculation device as claimed in claim 1 or 2, wherein,
Also possess Motion prediction unit, described Motion prediction unit to the surrounding of described vehicle, can the action of object of movement predict,
Described Motion prediction unit every predetermined discrete time to the surrounding of described vehicle, can the action of object of movement predict, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is larger in the aggregate value at all described place places, then more shorten described discrete time.
4. risk degree calculation device as claimed in claim 1 or 2, wherein,
Also possess Motion prediction unit, described Motion prediction unit to the surrounding of described vehicle, can the action of object of movement predict,
Described Motion prediction unit can calculate by the object of the movement probability that is present in each preposition place the surrounding of described vehicle, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is larger in the aggregate value at all described place places, then the dispersion of the distribution of described probability more increases.
5. risk degree calculation device as claimed in claim 3, wherein,
Also possess Motion prediction unit, described Motion prediction unit to the surrounding of described vehicle, can the action of object of movement predict,
Described Motion prediction unit can calculate by the object of the movement probability that is present in each preposition place the surrounding of described vehicle, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is larger in the aggregate value at all described place places, then the dispersion of the distribution of described probability more increases.
6. risk degree calculation device as claimed in claim 1 or 2, wherein,
Also possesses driving auxiliary unit, the driving of described driving auxiliary unit to the driver of described vehicle is assisted, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is higher in the aggregate value at all described place places, then described driving auxiliary unit more improves the degree of assisting the driving of the driver of described vehicle.
7. risk degree calculation device as claimed in claim 3, wherein,
Also possesses driving auxiliary unit, the driving of described driving auxiliary unit to the driver of described vehicle is assisted, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is higher in the aggregate value at all described place places, then described driving auxiliary unit more improves the degree of assisting the driving of the driver of described vehicle.
8. risk degree calculation device as claimed in claim 4, wherein,
Also possesses driving auxiliary unit, the driving of described driving auxiliary unit to the driver of described vehicle is assisted, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is higher in the aggregate value at all described place places, then described driving auxiliary unit more improves the degree of assisting the driving of the driver of described vehicle.
9. risk degree calculation device as claimed in claim 5, wherein,
Also possesses driving auxiliary unit, the driving of described driving auxiliary unit to the driver of described vehicle is assisted, and that described motive objects risk factor computing unit calculates, described motive objects risk factor is higher in the aggregate value at all described place places, then described driving auxiliary unit more improves the degree of assisting the driving of the driver of described vehicle.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/JP2010/057885 WO2011141984A1 (en) | 2010-05-10 | 2010-05-10 | Risk calculation apparatus |
Publications (2)
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| CN102884564A CN102884564A (en) | 2013-01-16 |
| CN102884564B true CN102884564B (en) | 2015-07-29 |
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| CN201080066680.7A Active CN102884564B (en) | 2010-05-10 | 2010-05-10 | Risk degree calculation device |
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| US (1) | US9170332B2 (en) |
| EP (1) | EP2571003B1 (en) |
| JP (1) | JP5403152B2 (en) |
| CN (1) | CN102884564B (en) |
| WO (1) | WO2011141984A1 (en) |
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| CN105103209B (en) * | 2013-04-10 | 2017-05-10 | 丰田自动车株式会社 | Detection device, detection method, driving assistance device, and driving assistance method |
| SE537621C2 (en) * | 2013-09-10 | 2015-08-11 | Scania Cv Ab | Detection of objects using a 3D camera and a radar |
| US9959766B2 (en) * | 2014-03-28 | 2018-05-01 | Nec Corporation | Information-collecting device, information-collection method, and program-recording medium |
| US9921307B2 (en) * | 2015-01-30 | 2018-03-20 | Toyota Motor Engineering & Manufacturing North America, Inc. | Combined RADAR sensor and LIDAR sensor processing |
| CA2983680C (en) * | 2015-04-23 | 2020-03-31 | Nissan Motor Co., Ltd. | Scene understanding device |
| US10726576B2 (en) * | 2016-06-29 | 2020-07-28 | Seeing Machines Limited | System and method for identifying a camera pose of a forward facing camera in a vehicle |
| JP6834704B2 (en) * | 2017-03-31 | 2021-02-24 | アイシン・エィ・ダブリュ株式会社 | Driving support device and computer program |
| CN111667720A (en) * | 2020-05-15 | 2020-09-15 | 腾讯科技（深圳）有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112356845B (en) * | 2020-11-19 | 2022-02-18 | 中国第一汽车股份有限公司 | Method, device and equipment for predicting motion state of target and vehicle |
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Also Published As
| Publication number | Publication date |
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| JP5403152B2 (en) | 2014-01-29 |
| US20130060548A1 (en) | 2013-03-07 |
| EP2571003B1 (en) | 2017-07-12 |
| CN102884564A (en) | 2013-01-16 |
| EP2571003A4 (en) | 2015-08-05 |
| JPWO2011141984A1 (en) | 2013-07-22 |
| US9170332B2 (en) | 2015-10-27 |
| EP2571003A1 (en) | 2013-03-20 |
| WO2011141984A1 (en) | 2011-11-17 |
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